Automotive vehicles are increasingly equipped with on-board restraint devices that deploy in the event that the vehicle rolls over in an attempt to provide added protection to occupants of the vehicle. For example, a pop-up roll bar can be deployed to extend vertically outward to increase the height of support provided by the roll bar, upon detecting an anticipated vehicle rollover condition. Additionally, many vehicles are typically equipped with multiple air bags, side curtains, and seatbelt pretensioners. These restraint devices require timely deployment to mitigate adverse effects to occupants in the vehicle. To achieve timely deployment of restraint devices, the dynamic motion of the vehicle must be determined and a decision must be made to determine whether a vehicle rollover is anticipated.
Various single sensor and multiple sensor modules have been employed in vehicles to sense the static and dynamic conditions of the vehicle. For example, tilt switches, tilt sensors, angular rate sensors, and linear accelerometers have been employed. One sophisticated rollover sensing approach employs up to six sensors including three accelerometers and three angular rate sensors (gyros) which are employed together for use in an inertial navigation system to track position and attitude of the vehicle. The sophisticated multiple sensor techniques generally employ discrimination algorithms implemented in a controller to process the sensed information and determine the potential for a vehicle overturn condition.
While various rollover sensing approaches have served well for anticipating some vehicle rollovers, there still exist certain rollover scenarios that are challenging to predict. In particular, several restraint devices have deployment decision times that require an early advance determination of an anticipated vehicle rollover. For example, a vehicle sliding sideways (in its lateral direction) and engaging a tripping surface on a roadway creates a scenario that requires an advance determination of an anticipated vehicle rollover, such that the restraint devices may need to be deployed quickly. In this scenario, the tripping device, such as a curb, may suddenly cause the vehicle to quickly rollover such that a quick determination of an anticipated vehicle rollover may be desirable even when the vehicle roll angle is less than five degrees (5°), for example.
One technique for sensing a vehicle overturn condition is disclosed in U.S. Pat. No. 6,038,495, entitled “VEHICLE ROLLOVER SENSING USING SHORT-TERM INTEGRATION,” the entire disclosure of which is hereby incorporated herein by reference. This technique employs an angular rate sensor for sensing roll rate of the vehicle. The sensed angular rate signal is integrated to produce an estimated current attitude angle, which is processed along with the sensed rate signal to predict a future roll angle. The predicted roll angle is compared to a threshold value to generate a deployment signal such that restraint devices may be deployed in response to the output signal.
While the aforementioned approach is generally well-suited for anticipating an overturn condition of the vehicle, a number of drawbacks still exist. For instance, angular rate sensors tend to be relatively complex and expensive. Additionally, in order to provide an acceleration value, the sensed rate signal must be differentiated, which generally is inherently error-prone.
It is therefore desirable to provide for a vehicle rollover sensing apparatus that may quickly predict an upcoming vehicle rollover. It is also desirable to provide for a cost effective vehicle roll sensing apparatus that accurately predicts a roll angle. It is further desirable to provide for a vehicle rollover sensing apparatus that may quickly predict an anticipated vehicle rollover condition, far enough in advance to deploy restraint device(s) in certain driving scenarios.